%global _empty_manifest_terminate_build 0 Name: python-UpSetPlot Version: 0.8.0 Release: 1 Summary: Draw Lex et al.'s UpSet plots with Pandas and Matplotlib License: BSD-3-Clause URL: https://upsetplot.readthedocs.io Source0: https://mirrors.nju.edu.cn/pypi/web/packages/25/62/f9ab73c23da63d77e8498253b043d03c65c259f4d0358309b37f56cdf5cd/UpSetPlot-0.8.0.tar.gz BuildArch: noarch %description |version| |licence| |py-versions| |issues| |build| |docs| |coverage| This is another Python implementation of UpSet plots by Lex et al. [Lex2014]_. UpSet plots are used to visualise set overlaps; like Venn diagrams but more readable. Documentation is at https://upsetplot.readthedocs.io. This ``upsetplot`` library tries to provide a simple interface backed by an extensible, object-oriented design. There are many ways to represent the categorisation of data, as covered in our `Data Format Guide `_. Our internal input format uses a `pandas.Series` containing counts corresponding to subset sizes, where each subset is an intersection of named categories. The index of the Series indicates which rows pertain to which categories, by having multiple boolean indices, like ``example`` in the following:: >>> from upsetplot import generate_counts >>> example = generate_counts() >>> example cat0 cat1 cat2 False False False 56 True 283 True False 1279 True 5882 True False False 24 True 90 True False 429 True 1957 Name: value, dtype: int64 Then:: >>> from upsetplot import plot >>> plot(example) # doctest: +SKIP >>> from matplotlib import pyplot >>> pyplot.show() # doctest: +SKIP makes: And you can save the image in various formats:: >>> pyplot.savefig("/path/to/myplot.pdf") # doctest: +SKIP >>> pyplot.savefig("/path/to/myplot.png") # doctest: +SKIP This plot shows the cardinality of every category combination seen in our data. The leftmost column counts items absent from any category. The next three columns count items only in ``cat1``, ``cat2`` and ``cat3`` respectively, with following columns showing cardinalities for items in each combination of exactly two named sets. The rightmost column counts items in all three sets. Rotation We call the above plot style "horizontal" because the category intersections are presented from left to right. `Vertical plots `__ are also supported! Distributions Providing a DataFrame rather than a Series as input allows us to expressively `plot the distribution of variables `__ in each subset. Loading datasets While the dataset above is randomly generated, you can prepare your own dataset for input to upsetplot. A helpful tool is `from_memberships`, which allows us to reconstruct the example above by indicating each data point's category membership:: >>> from upsetplot import from_memberships >>> example = from_memberships( >>> example cat0 cat1 cat2 False False False 56 True 283 True False 1279 True 5882 True False False 24 True 90 True False 429 True 1957 dtype: int64 See also `from_contents`, another way to describe categorised data, and `from_indicators` which allows each category to be indicated by a column in the data frame (or a function of the column's data such as whether it is a missing value). %package -n python3-UpSetPlot Summary: Draw Lex et al.'s UpSet plots with Pandas and Matplotlib Provides: python-UpSetPlot BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-UpSetPlot |version| |licence| |py-versions| |issues| |build| |docs| |coverage| This is another Python implementation of UpSet plots by Lex et al. [Lex2014]_. UpSet plots are used to visualise set overlaps; like Venn diagrams but more readable. Documentation is at https://upsetplot.readthedocs.io. This ``upsetplot`` library tries to provide a simple interface backed by an extensible, object-oriented design. There are many ways to represent the categorisation of data, as covered in our `Data Format Guide `_. Our internal input format uses a `pandas.Series` containing counts corresponding to subset sizes, where each subset is an intersection of named categories. The index of the Series indicates which rows pertain to which categories, by having multiple boolean indices, like ``example`` in the following:: >>> from upsetplot import generate_counts >>> example = generate_counts() >>> example cat0 cat1 cat2 False False False 56 True 283 True False 1279 True 5882 True False False 24 True 90 True False 429 True 1957 Name: value, dtype: int64 Then:: >>> from upsetplot import plot >>> plot(example) # doctest: +SKIP >>> from matplotlib import pyplot >>> pyplot.show() # doctest: +SKIP makes: And you can save the image in various formats:: >>> pyplot.savefig("/path/to/myplot.pdf") # doctest: +SKIP >>> pyplot.savefig("/path/to/myplot.png") # doctest: +SKIP This plot shows the cardinality of every category combination seen in our data. The leftmost column counts items absent from any category. The next three columns count items only in ``cat1``, ``cat2`` and ``cat3`` respectively, with following columns showing cardinalities for items in each combination of exactly two named sets. The rightmost column counts items in all three sets. Rotation We call the above plot style "horizontal" because the category intersections are presented from left to right. `Vertical plots `__ are also supported! Distributions Providing a DataFrame rather than a Series as input allows us to expressively `plot the distribution of variables `__ in each subset. Loading datasets While the dataset above is randomly generated, you can prepare your own dataset for input to upsetplot. A helpful tool is `from_memberships`, which allows us to reconstruct the example above by indicating each data point's category membership:: >>> from upsetplot import from_memberships >>> example = from_memberships( >>> example cat0 cat1 cat2 False False False 56 True 283 True False 1279 True 5882 True False False 24 True 90 True False 429 True 1957 dtype: int64 See also `from_contents`, another way to describe categorised data, and `from_indicators` which allows each category to be indicated by a column in the data frame (or a function of the column's data such as whether it is a missing value). %package help Summary: Development documents and examples for UpSetPlot Provides: python3-UpSetPlot-doc %description help |version| |licence| |py-versions| |issues| |build| |docs| |coverage| This is another Python implementation of UpSet plots by Lex et al. [Lex2014]_. UpSet plots are used to visualise set overlaps; like Venn diagrams but more readable. Documentation is at https://upsetplot.readthedocs.io. This ``upsetplot`` library tries to provide a simple interface backed by an extensible, object-oriented design. There are many ways to represent the categorisation of data, as covered in our `Data Format Guide `_. Our internal input format uses a `pandas.Series` containing counts corresponding to subset sizes, where each subset is an intersection of named categories. The index of the Series indicates which rows pertain to which categories, by having multiple boolean indices, like ``example`` in the following:: >>> from upsetplot import generate_counts >>> example = generate_counts() >>> example cat0 cat1 cat2 False False False 56 True 283 True False 1279 True 5882 True False False 24 True 90 True False 429 True 1957 Name: value, dtype: int64 Then:: >>> from upsetplot import plot >>> plot(example) # doctest: +SKIP >>> from matplotlib import pyplot >>> pyplot.show() # doctest: +SKIP makes: And you can save the image in various formats:: >>> pyplot.savefig("/path/to/myplot.pdf") # doctest: +SKIP >>> pyplot.savefig("/path/to/myplot.png") # doctest: +SKIP This plot shows the cardinality of every category combination seen in our data. The leftmost column counts items absent from any category. The next three columns count items only in ``cat1``, ``cat2`` and ``cat3`` respectively, with following columns showing cardinalities for items in each combination of exactly two named sets. The rightmost column counts items in all three sets. Rotation We call the above plot style "horizontal" because the category intersections are presented from left to right. `Vertical plots `__ are also supported! Distributions Providing a DataFrame rather than a Series as input allows us to expressively `plot the distribution of variables `__ in each subset. Loading datasets While the dataset above is randomly generated, you can prepare your own dataset for input to upsetplot. A helpful tool is `from_memberships`, which allows us to reconstruct the example above by indicating each data point's category membership:: >>> from upsetplot import from_memberships >>> example = from_memberships( >>> example cat0 cat1 cat2 False False False 56 True 283 True False 1279 True 5882 True False False 24 True 90 True False 429 True 1957 dtype: int64 See also `from_contents`, another way to describe categorised data, and `from_indicators` which allows each category to be indicated by a column in the data frame (or a function of the column's data such as whether it is a missing value). %prep %autosetup -n UpSetPlot-0.8.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-UpSetPlot -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 11 2023 Python_Bot - 0.8.0-1 - Package Spec generated